Financial Well-Being 1 Running head: FINANCIAL WELL-BEING TCAI Working Paper 5-2 Financial Well-Being: Descriptors and Pathways
Bibliographic record
Abstract
Currently there exists the highest ever rate of borrowing in the developed world. Average personal debt equals average disposable income in Canada, and outstanding credit has doubled between 1997 and 2002 (Lewyckwj, 2002). In the UK, average personal debt now far outstrips average disposable income, and the country now has one of the fastest debt growth rates in the developed world (Cowell, 2003). In the US, the amount of non-mortgage debt is so high that is represents $7500 debt for every household in the country (Herman, 2000). Approximately 1.5 million Americans file for bankruptcy in each year (Toomey, 2003). As is evident, the size of the debt of the average citizen of an industrialized nation has reached epic proportions, and this is especially true for Americans. While the topic of indebtedness has become fodder for many talk shows and the cause of much economic alarm, however, research in the area is limited. Debt has financial consequences that last beyond the period of indebtedness. A history of debt can affect the credit rating of an individual, affecting their ability to qualify for a home mortgage, purchase a vehicle, and receive bank loans and other financial services. Financial and economic difficulties also have psychological and
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".